Food Engineering Reviews最新文献

Traditional assessment (e.g., visual inspection and biochemical analysis) is the prevailing method for meat quality assessment in the food industry. However, this approach is time-consuming, laborious, costly, and subjective. In response to the inherent limitations associated with conventional assessment, RGB (red–green–blue) cameras, hyperspectral imaging, and structured illumination reflectance imaging are gaining ample attention in the food industry. These techniques are increasingly applied to various aspects of meat quality and safety assessments, encompassing parameters such as tenderness, chemical composition, adulteration, and overall quality traits. This review focuses on scientific articles published in the past five years that leverage these machine vision techniques to address challenges in the meat processing industry. These machine-vision techniques are briefly introduced, shedding light on their principles and applications. Moreover, this review identifies the challenges and strengths associated with these technologies. To provide comprehensive insights, this review includes thoughtful solutions to overcome the challenges posed by these advanced techniques in the context of meat quality assessment within the food industry. Furthermore, we suggest a novel approach for meat processing which is integrating hyperspectral imaging with structured illumination reflectance imaging for easy detection of both surface and internal quality assessment in meat.

The incorporation of Artificial Intelligence (AI) could deliver a new era in food manufacturing, marked by increased operational efficiencies, higher product quality, and better safety standards. This review offers an in-depth examination of the field's evolution, outlines the leading AI methodologies, and investigates their applications in food manufacturing. This review begins with an introduction to AI and its historical context before classifying the main AI methods used in food manufacturing as machine learning, computer vision, robotics, and natural language processing. Machine learning has emerged as an AI application in many areas of food manufacturing due to its ability to learn from data and make predictions. Computer vision is a popular form of AI and plays an important role in visual inspections, ensuring product consistency and detecting defects. Robotics, in conjunction with AI, has automated a wide range of labour-intensive tasks, from packaging to palletizing, resulting in significant improvements in operational efficiency. Natural language processing has found applications in customer service and compliance, allowing for more efficient interactions and regulatory compliance. AI applications in food manufacturing are numerous and diverse and key areas such as ingredient sorting, quality assessment, process optimization, and supply chain management are highlighted in this review. Finally, we present issues that the industry is encountering in the implementation of AI, as well as a research agenda based on the findings. In-depth analysis provided in this review including the field's evolution, main AI methods used, and their applications in food manufacturing, can provide valuable insights for researchers, practitioners, and decisionmakers in applications of AI in food manufacturing.

As an innovative dehydration technique that combines effectiveness, energy conservation, and preserves quality of product the Refractance Window™ (RW) drying offers a competitive substitute for conventional drying techniques like tray, vacuum, and drum drying. This comprehensive review analyzes the fundamental mechanism, heat transfer dynamics, and the novel hybrid methodologies of RW drying. Key findings emphasize the distinct mechanisms of heat transfer through thermal conduction, convection, and radiation, which enhance drying kinetics while reducing nutrient degradation and energy consumption. Furthermore, hybrid RW drying systems that integrate techniques such as infrared, osmotic, microwave, solar and ultrasound drying demonstrate enhanced performance, particularly in decreasing time for drying and enhancing drying efficiency and quality of food products. A comparative examination highlights that the hybrid approach increased the efficiency and scalability of RW drying which is better for industrial applications. RW dryers are useful for handling high-protein foodstuffs, preparing fruit leather and edible films, improving the gelling and emulsion qualities, and drying leafy vegetables or marine foods without compromising their nutritional content. Despite its benefits, difficulties like as material-specific adaptations, energy optimization, and scaling up processes remain subjects for more investigation. The majority of research on RW drying concentrates on the determining the quality of product. This study consolidates existing knowledge, patents and opens up key research gaps, delivering some practical insights for developing the design and implementation of RW drying systems.

3D (three-dimensional) food printing has emerged as a transformative technology, offering exceptional adaptability and customization across various industries. This review explores its potential to enhance environmental sustainability by minimizing food waste, improving portion control, and promoting eco-friendly practices. Key technological foundations, such as rheological assessments for material flow optimization, colorimetric analysis for accurate color representation, and advanced material handling techniques for consistent texture and nutrition, are critically examined. Moreover, it highlights the synergy between mechanical precision, algorithmic control, and material science, illustrating how these elements collectively ensure adherence to high-quality and safety standards in food engineering. The study also reviews 3D food printing technologies, methods, materials, and applications, emphasizing the pivotal role of customization in addressing diverse dietary, cultural, medical, and aerospace needs. A SWOT (strengths, weaknesses, opportunities, and threats) analysis evaluates the current capabilities and limitations of the technology, identifying challenges and future prospects. This comprehensive analysis underscores the potential of 3D food printing to revolutionize food production, offering valuable insights for researchers, practitioners, and policymakers.

The field of probiotics has garnered significant attention due to their potential health benefits, such as enhancing gut health and immune response. The effective delivery of probiotics through microencapsulation is crucial due to the sensitivity of probiotics to environmental stresses. Mathematical modeling offers profound insights into the growth, survival, inactivation, and release dynamics of microencapsulated probiotics, providing a basis for optimizing formulation and predicting behavior under various conditions. Although some of these subjects are cross-sectionally imported from the field of drug delivery, there are no specific papers that comprehensively discuss the mathematical modeling of probiotic microencapsulation from production to delivery. Thus, this paper aims to review the current landscape of mathematical models utilized in the microencapsulation of probiotics in different timelines, including cell culturing, microencapsulation process, storage, and delivery in the gastrointestinal tracts. Predictive microbial models, empirical reaction models, and drug release models were highlighted in each activity. In addition to the principles and equations, recent studies on the application of kinetic models for probiotics and their microcapsules were summarized and discussed. The review emphasizes the necessity of a mathematical approach for the comprehensive understanding of microencapsulated probiotics, combined with insights into food chemistry, microbiology, and engineering, to innovate and advance the application of probiotics for health improvement.

Physical drying is an emerging technology praised for its energy efficiency, environmental friendliness, and ease of control. However, advancing this technology requires a deeper understanding of the underlying physics. Currently, the principles of physical drying and its interactions with food are not well understood, and uneven drying remains an issue. This paper reviews the physical principles of the interaction between physical fields and food during the drying process, the application of physical fields in drying, and the theoretical models of physical field drying. It summarizes the main causes of physical rmity and the process of optimizing physical fields through numerical simulation methods. It explores the principles and models of multi-physical field hybrid drying and their enhancement of drying performance. Physical field drying is superior to traditional drying mainly because physical fields utilize mechanical waves, electromagnetic waves, and electric fields to act on both the interior and exterior of food, enhancing heat and mass transfer to achieve improved drying effects. A thorough understanding of physical field drying principles and theoretical models helps us comprehend various physical phenomena in physical field drying while optimizing and accelerating the drying process. The non-uniformity issues in physical fields can be resolved through numerical simulation methods to optimize physical field parameters and design. Finally, physical fields can compensate for their respective deficiencies through physical field combinations, further improving the drying effect of food.

For more than a century, the dairy industry has used high-pressure homogenization for size reduction of fat globules. The prevailing break-up mechanism, turbulence, has been thoroughly investigated and the equipment continuously optimized thereafter. However, the high-pressure homogenizer is also used in size reduction of plant cell structures, for example in production lines of plant-based beverages, fruit and vegetable juices and ketchup. This review will provide a scientific basis for homogenization of plant-based materials with focus on break-up mechanisms. A cross-study comparison shows that different raw materials break in different ways, e.g. individual cells breaking into cell wall fragments and cell clusters breaking into smaller cell clusters. In general, raw materials which after intense premixing exist as cell clusters are more difficult to break than raw materials existing as individual cells. The resistance to break-up also appears to follow ‘raw material hardness’, where harder raw materials, e.g., parsnip and almond, are more difficult to break than softer raw materials, e.g., strawberry and orange. It can also be concluded that the initial particle size is of large importance for the size after high pressure homogenization. It is concluded that little is known about the break-up mechanism(s). Much does, however, point towards the mechanism being different from that of emulsion drop break-up. Suggestions for future studies, both regarding fundamental understanding (e.g., cell strength and breakup, HPH mechanistic studies and break up visualisations) and industrial applications (e.g., energy optimal operation, device design and wear) are provided.

Food drying is a critical process in food preservation, directly impacting the quality, energy consumption, and environmental sustainability of the final product. Traditional optimization techniques, while useful, often fall short in addressing the complex, nonlinear, and multi-objective nature of food drying. Nature-inspired algorithms, which mimic biological, chemical, and physical systems, have emerged as powerful tools for optimizing these processes, demonstrating superior performance in handling multiple parameters and conflicting objectives. This review critically examines the application of various nature-inspired optimization approaches in food drying, including artificial neural networks, genetic algorithms, particle swarm optimization, and non-dominated sorting genetic algorithm II. The review highlights the theoretical underpinnings of these algorithms, their specific applications in food drying, and the advantages and limitations of each method. Recent case studies are also discussed to illustrate the practical implementation of these techniques in improving product quality, energy efficiency, and environmental impact in food processing plants. The findings highlight the potential of integrating these advanced optimization algorithms into computer-integrated manufacturing systems, driving the food drying industry toward more sustainable and cost-effective practices. Additionally, the scalability of these methods for industrial applications is critically evaluated, identifying practical barriers and suggesting pathways for future research. This comprehensive review aims to serve as a valuable resource for researchers and practitioners interested in the latest developments in food drying optimization, providing insights that are both broad and deep, suitable for a multidisciplinary audience.

This study explores the enhancement of quality traceability in the food processing industry through the integration of modern digital tools, specifically blockchain technology. By combining a thorough literature review with the analysis of real-case studies, the research investigates current digital trends and their practical applications in the food processing sector. The findings show that blockchain-based approaches significantly improve supply chain transparency and quality management. Despite the potential benefits, the study also identifies challenges in practical implementations, such as resistance to adoption and the need for substantial investment in digital infrastructure. The research highlights the limited cultural attitude within the industry towards the comprehensive adoption of these modern tools, with their usage mostly confined to isolated case studies rather than a structured, widespread experimental orientation. Practical implications include providing businesses with guidelines for implementing digital tools to enhance quality traceability and management. Social implications underscore the critical role of these tools in meeting societal demands for food safety and transparency, particularly regarding information on raw materials, processing, and preservation methods. Thus, this paper offers a comprehensive overview of the use of blockchain and other digital tools to improve quality traceability in the food processing industry, contributing valuable insights and guidelines for future implementations.

Grain legumes such as pea, faba bean, lupin and soybean are an important protein source for the production of plant-based foods and thus facilitate the protein transition. For many food applications, the proteins are first isolated using conventional wet methods that are resource intensive. Dry fractionation processes are therefore developed to facilitate a more sustainable protein transition. This review discusses the status of dry fractionation of grain legumes to produce protein-rich ingredients for food production and how the use of these dry-enriched ingredients could be further enhanced. Dry fractionation includes dry milling and dry separation technologies which are first briefly described. There are different strategies to further improve the separation, which include pre-treatments and improving powder bulk behaviour. Pre- and post-treatments not only improve the functional properties of dry-enriched protein ingredients but also enhance the nutritional value of the ingredients and minimize off-flavours. Opportunities still exist to further optimise dry fractionation techniques and pre-treatments to increase the purity and yield. Finally, the use of dry-enriched fractions should be accelerated by development of 1) functionality-driven ingredient formulation strategies and 2) new physical post-modifications and food fermentation strategies to enhance functionality, nutritional value and taste of the ingredients to prepare attractive food products.